S-PLM-TM weights: backbone, TM head, EC head, alignment head

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LICENSE

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+S-PLM-TM
+========
+
+Copyright (c) 2026 Xudong Lab, University of South Florida. All rights reserved.
+
+Required Notice: Copyright (c) 2026 Xudong Lab, University of South Florida - S-PLM-TM
+
+Licensed under the PolyForm Noncommercial License 1.0.0 (full text below).
+In short: you may use, copy, modify and redistribute this work for any
+NONCOMMERCIAL purpose -- academic research, teaching, and work at non-profit,
+educational, public-research or government institutions -- provided you keep
+this notice. COMMERCIAL USE IS NOT GRANTED by this license; contact the authors
+to discuss commercial terms.
+
+WHAT THIS LICENSE COVERS
+------------------------
+  code/          this release's API and the model definition code
+  models/        the backbone checkpoint, TM head, EC head and align head weights
+  reproduce/     the reproduction scripts and their recorded results
+  README.md, models/MODEL_CARD.md and the other documentation in this tree
+
+WHAT IT DOES NOT COVER (third-party material, under its own terms)
+------------------------------------------------------------------
+  reproduce/ec_deer_figure7/data/
+      DEER's Figure 7 benchmark data (Liu et al., Cell Host & Microbe 2026).
+      Licensed by its authors under the Apache License 2.0 -- a permissive
+      license that we cannot and do not restrict. Those files remain under
+      Apache-2.0 for everyone, including commercial users. See that directory's
+      LICENSE.DEER and README.md.
+
+  pfam_data/Pfam-A.clans.tsv
+      Pfam data (Pfam release 36.0), released by the Pfam consortium under
+      CC0 1.0 (public domain). Redistributed here unchanged; it is NOT covered
+      by the noncommercial terms above and remains CC0 for everyone, including
+      commercial users. Cite: Mistry, J. et al. Nucleic Acids Res. 49,
+      D412-D419 (2021).
+
+  PfamScan and the Pfam-A HMM library
+      Not bundled, on purpose. PfamScan (pfam_scan.pl) is GPL-2.0-or-later, so
+      bundling it would pull GPL code into this package; the HMM library is
+      300 MB. code/scripts/setup_pfam.sh downloads both on demand into
+      pfam_data/, where they stay under their own licenses.
+
+  The ProtT5-XL encoder (Rostlab/prot_t5_xl_uniref50), which the backbone is
+      built on, is fetched at run time and is not redistributed here. The
+      ProtTrans project is MIT-licensed.
+
+IF YOU USE THIS WORK, ACKNOWLEDGE IT
+------------------------------------
+Cite the S-PLM-TM paper (see README.md) in any publication, preprint, poster or
+report whose results depend on this software or these weights.
+
+IF YOU MODIFY IT
+----------------
+You may. Please mark your changes clearly and do not present a modified version
+as the official S-PLM-TM, so that published numbers stay traceable to a known
+version. Neither the name "S-PLM", the University of South Florida, nor the names
+of the authors may be used to endorse or promote products derived from this
+software without specific prior written permission.
+
+--------------------------------------------------------------------------------
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README.md

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+---
+license: other
+license_name: polyform-noncommercial-1.0.0
+license_link: https://polyformproject.org/licenses/noncommercial/1.0.0
+library_name: pytorch
+tags:
+  - protein-language-model
+  - protein
+  - biology
+  - remote-homology
+  - structure-aware
+  - retrieval
+---
+
+# S-PLM-TM — model weights
+
+Structure-aware protein sequence embeddings. One sequence in, a 1024-d vector out,
+in three flavours — plus a residue-level head for alignment.
+
+**These are the weights only.** The code, model cards and reproduction scripts live in
+the GitHub repository: `https://github.com/duolinwang/S-PLM-TM`
+
+> ⚠️ **Noncommercial license.** Academic, teaching and nonprofit/educational/
+> public-research/government use is granted; **commercial use is not**.
+> Full text: **`LICENSE` in this repository** (PolyForm Noncommercial 1.0.0).
+> Cite the paper if you use this.
+>
+> The weights are public, but **the code repository is currently private** while the
+> paper is under preparation — the GitHub links below will 404 unless you have access.
+> Ask the authors if you need it before then.
+
+## What's here
+
+| File | Size | What it is |
+|---|---|---|
+| `backbone/splm_tm_backbone.pth` | 2.2 GB | The encoder. **Slim archive** — the frozen ProtT5-XL trunk is *not* included; it is fetched by `from_pretrained` at run time |
+| `tm_head/tm_calib_head.pt` | 12 MB | TM calibration head → `emb_tm`, L2-normalised, inner product ≈ predicted TM-score |
+| `ec_head/ec_head.pt` | 8.4 MB | EC retrieval head → `emb_ec`. ⚠️ It consumes `emb_h` (TM head output), **not** `emb_raw` |
+| `align_head/align_head.pt` | 17 MB | Residue-level alignment head. Consumes per-residue features, **not** a pooled vector |
+
+The architecture YAML is **not** here — it is text, so it lives in git (`models/backbone/` in the GitHub repo) where it has a history and a visible diff. Keeping a second copy here would let the two drift apart silently.
+
+Every head is **paired with this exact backbone checkpoint**. Swapping the checkpoint
+without swapping the heads produces silently meaningless vectors.
+
+## Quick start
+
+```bash
+git clone https://github.com/duolinwang/S-PLM-TM.git && cd S-PLM-TM
+pip install -r requirements.txt
+hf download deepduoduo/S-PLM-TM --local-dir models/
+
+python code/scripts/embed.py --fasta my.fasta --modes raw tm ec --out out.npz
+```
+
+```python
+d = np.load("out.npz", allow_pickle=True)
+d["emb_raw"]   # [N,1024] pre-projector, unnormalised — use this to train downstream models
+d["emb_tm"]    # [N,1024] L2-normalised — structure / remote-homology retrieval
+d["emb_ec"]    # [N,1024] L2-normalised — EC / functional retrieval
+```
+
+The three are **chained, not parallel**:
+
+```
+sequence --backbone--> emb_raw --TM head--> emb_tm (= emb_h) --EC head--> emb_ec
+```
+
+so `--modes ec` pulls `tm` in automatically, and one forward pass gives you all three.
+
+## Things that will bite you
+
+1. **Output is 1024-d, not 1280.** A 1280-d vector means the backbone was built as
+   ESM-2 by mistake.
+2. **Embedding defaults to full sequences.** Set `--max-length N` to truncate;
+   `0` also disables truncation. Some published numbers (the EC head's DEER
+   Figure-7 results) were measured with **`--max-length 1024`**. Match that setting
+   before comparing.
+3. **"Skipped 196 frozen trunk tensors" on load is normal** — that is the slim archive
+   working as intended; those tensors come from `from_pretrained`.
+4. **`emb_ec` is not an EC-number prediction.** The EC head is a *retrieval* head
+   trained with a triplet loss; there are no class logits and no probabilities.
+5. ProtT5-XL (~11 GB) is downloaded on first use. Set `HF_HOME` to somewhere with
+   space before running on a cluster.
+
+## Verify your download
+
+md5 checksums for every file are in `models/DOWNLOAD.md` in the GitHub repo.
+The numbers reported in `models/MODEL_CARD.md` were measured with exactly these files.
+
+## Reproducibility
+
+`reproduce/ec_deer_figure7/` in the GitHub repo reproduces the EC head's published
+DEER Figure-7 numbers end to end (re-encoding all 4,303 sequences from FASTA, not
+reusing cached embeddings). Verified bit-for-bit: hit counts 222/323/378 and all 90
+per-family recall values identical to the reference.
+
+## Citation
+
+See the GitHub repository's README for the paper citation.

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